The way we manage supply chains has really changed over time, moving from traditional methods to more advanced cloud-native solutions that handle today’s complex global operations. One standout player in this space is Oracle Cloud Infrastructure (OCI). It's a powerful platform that helps businesses create and run cloud-native applications specifically designed for their supply chain needs. In this study, we delve into how OCI is transforming supply chain processes. It leverages features like high-performance computing, an autonomous database, and Kubernetes-based containerization, along with various integration tools. Through a mix of qualitative and quantitative analyses from real-world use cases, we showcase the benefits that OCI brings to the table in terms of scalability, efficiency, and cost savings. Our findings shine a light on how OCI enables real-time data processing and predictive analytics, allowing businesses to make informed decisions quickly. It also makes it easier to integrate different parts of the supply chain, all while addressing important concerns like security and compliance. Overall, this research highlights the potential of OCI to reshape supply chain management, and it opens up exciting avenues for exploring new technologies within the OCI ecosystem.

From its status of a simple supportive operational function in today’s business world, product management has grown into a strategic value creating activity. These changes are due to technological developments, and changes in consumer behavior and increased market competition. In analyzing the article, it considers the primary driving forces of this change with emphasis on the approaches under which organizations are adopting innovation in the dynamic and unpredictable market environment. At the core of this transformation are data-driven approaches, Agile development methodologies and customers’ orientated methodologies, which have now directed the whole product process and the decision-making process of the product cycles.

Based on examples from best practice in this area, as well as bibliographic research, this article identifies innovation streams that are recalibrating this profession: AI, cross-functional teaming, and sustainability. It also examines issues that organizations encounter, including resistance to change, absence of or inadequate skills, and the dual issue of innovation and productivity.

The outcomes revealed reveal the need to focus on managing changes and volatility, and lifelong learning to achieve sustainable competitiveness. Realizing that the business environment is rapidly changing, organizations identify product management to successfully navigate it, while specifying the directions for developing technological potential in line with market demand. Herein, we provide practical recommendations and side notes for organizations and product managers who intend to thrive in this dynamic field.

The utilization of Artificial Intelligence (AI) in current systems carries significant dependence upon the stability of data engineering technique. This article discusses the importance of improving the efficiency of data pipelines to improve AI applications with reference to some sample predictive analytics analytics. Therefore, through reviewing various real-world applications in the article, critical issues like data latency, inconsistency, and scalability, which affect the value of AI models, have been noted. These yield problems are discussed and real-time data processing, autopipe, and other data engineering methods that deal with such problems are explained in its details. These shed light on how the practices enhance accuracy of AI model, operations efficiency and real time decisions. This study therefore makes a call for fine tuning of data pipelines in the effort to achieve optimal usage of AI in various fields.